Lifestyle Interventions and Prevention of Suicide
Bibliographic record
Abstract
Over the past years, there has been a growing interest in the association between lifestyle psychosocial interventions, severe mental illness, and suicide risk. Patients with severe mental disorders have higher mortality rates, poor health states, and higher suicide risk compared to the general population. Lifestyle behaviors are amenable to change through the adoption of specific psychosocial interventions, and several approaches have been promoted. The current article provides a comprehensive review of the literature on lifestyle interventions, mental health, and suicide risk in the general population and in patients with psychiatric disorders. For this purpose, we investigated lifestyle behaviors and lifestyle interventions in three different age groups: adolescents, young adults, and the elderly. Several lifestyle behaviors including cigarette smoking, alcohol use, and sedentary lifestyle are associated with suicide risk in all age groups. In adolescents, growing attention has emerged on the association between suicide risk and internet addiction, cyberbullying and scholastic and family difficulties. In adults, psychiatric symptoms, substance and alcohol abuse, weight, and occupational difficulties seems to have a significant role in suicide risk. Finally, in the elderly, the presence of an organic disease and poor social support are associated with an increased risk of suicide attempt. Several factors may explain the association between lifestyle behaviors and suicide. First, many studies have reported that some lifestyle behaviors and its consequences (sedentary lifestyle, cigarette smoking underweight, obesity) are associated with cardiometabolic risk factors and with poor mental health. Second, several lifestyle behaviors may encourage social isolation, limiting the development of social networks, and remove individuals from social interactions; increasing their risk of mental health problems and suicide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".